Undergraduate Course: Mathematical Programming in Advanced Analytics (BUST10134)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course will provide students with the foundations of prescriptive analytics with emphasis on mathematical programming concepts, applications, models, and solution methods. (This course was formerly entitled Mathematical Programming BUST10011.) |
| Course description |
Optimisation problems are concerned with optimising an objective function subject to a set of constraints. When deterministic optimisation problems are translated in algebraic form, we refer to them as mathematical programs. Mathematical programming, as an area within Operational Research (OR), Management Science (MS) and Business Analytics (BA), is concerned with model building and strategies and methods for solving mathematical programs. In this course, we address model building in OR/MS/BA, present a variety of typical OR/MS/BA problems and their mathematical programming formulations, provide general tips on how to model managerial situations, and discuss solution strategies and present solution methods for linear programs, non-linear programs and integer programs. Last, but not least, students will learn how to use/build prescriptive analytics tools in the context of decision problems faced by business managers. The four main topics covered in this course are:
Syllabus
1. Introduction to OR/MS and Model Building;
2. Linear Programming (LP): Review of basic concepts and methods; namely, the simplex method and the dual simplex method, sensitivity analysis, and duality theory;
3. Integer Programming (IP): Basic concepts, relationship with linear programming, strategies and methods of solving integer programs; namely, brand-and-bound algorithms, cutting plane algorithms, and brand-and-cut algorithms;
4. Non-linear Programming (NLP): Basic concepts, relationship with linear programming, strategies and methods of solving unconstrained and constrained non-linear programs.
PLANNED STUDENT LEARNING EXPERIENCES
This lecture and tutorial programme, which builds on knowledge from Management Science & Business Analytics courses in earlier years, develops mathematical programming model building and solution techniques, and is supported by mandatory readings and supervised discussion sessions. These supervised sessions aim at discussing how to put into practice the concepts and methods presented in the lectures and learned from the mandatory readings and the term projects. In addition, these sessions also serve as advice/support sessions so that students can seek feedback on their term projects work-in-progress. The student experience requires active learning and engagement, which requires students to read relevant chapters in the textbooks and other sources before attending classes. Students are required to complete three group projects using MatLab. Besides attending lectures and supervised discussion sessions (both of which are compulsory), students will work in groups on realistic projects (groups will be formed by the lecturer to reflect a heterogeneity of skills required for the projects) and present their work in class to an audience that may include practitioners and term project providers. Guest speakers might be invited for the benefit of students, however, students should not expect any hand-outs from the guests.
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Information for Visiting Students
| Pre-requisites | A pass in Management Science and Information Systems (BUST08007) OR Management Science and Operations Planning (BUST10020) equivalents.
Visiting students should have at least 3 Business Studies courses at grade B or above (or be predicted to obtain this). We will only consider University/College level courses. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2016/17, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 8,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
168 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
90 %,
Practical Exam
10 %
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| Additional Information (Assessment) |
Three x (up to) 3,500-word group projects on prescriptive business analytics (90%) covering different aspects of the course. The aim of the projects is to allow students to 'learn by doing' and to enhance their skills in using state-of-the-art prescriptive analytics tools in the context of decision problems faced by business managers. For each group project, students are expected to produce reports with both academic rigour and managerial insight.
The group projects are as follows:
1. First project on business analytics with linear programming involving model building and the use of the material on linear programming covered in the lectures to address a decision making problem or business issue (30%, including peer assessment);
2. Second project on business analytics with integer programming involving model building and the use of the material on integer programming covered in the lectures to address a decision making problem or business issue (30% including peer assessment);
3. Third project on business analytics with nonlinear programming involving model building and the use of the material on nonlinear programming covered in the lectures to address a decision making problem or business issue (30% including peer assessment).
All projects involve the writing of a report in which students are expected to explain/document the mathematical formulation of the problem under consideration, explain/document the solution method chosen or proposed in a way that is accessible to both technical and non-technical audiences, interpret solutions, formulate managerial guidelines, and make recommendations.
A presentation of all three projects including oral examination of technical knowledge; that is, methodologies and methods, as well as prescriptive analytics tools used (10%)
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Assess critically the utility of a number of mathematical programming techniques.
- Describe mathematical programming solution strategies and techniques.
- Use mathematical programming methods to address management decision problems.
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Reading List
Recommended Reading:
1. S. P. Bradley, A. C. Hax, and T. L. Magnanti (1977), Applied Mathematical Programming, Addison-Wesley. [JCM Library shelfmark QA402.5 Bra; copy on order for Main Library HUB Reserve};
2. M. S. Bazaraa, H. D. Sherali, C. M. Shetty (2006), Nonlinear Programming: Theory and Algorithms, third edition, Wiley. [Copy in Main Library HUB Reserve shelfmark T57.8 Baz]. |
Additional Information
| Course URL |
http://www.bus.ed.ac.uk/programmes/ugpc.html |
| Graduate Attributes and Skills |
Cognitive Skills
On completion of the course students should:
(i) demonstrate ability in deciding whether a problem is amenable to solution by mathematical programming techniques;
(ii) demonstrate ability in using mathematical programming solution techniques;
(iii) demonstrate ability in explaining the solution to mathematical programming models.
Key Skills
On completion of the course students should:
(i) be able to formulate problems in mathematical programming terms;
(ii) be able to solve mathematical programming problems using commercial software;
(iii) be able to communicate mathematical programming solutions to non-specialists.
Subject Specific Skills
On completion of the course students should:
(i) have extended their model building skills;
(ii) have increased their model solution skills. |
| Keywords | Mathematical Programming in Advanced Analytics |
Contacts
| Course organiser | Dr Jamal Ouenniche
Tel: (0131 6)50 3792
Email: Jamal.Ouenniche@ed.ac.uk |
Course secretary | Ms Patricia Ward-Scaltsas
Tel: (0131 6)50 3823
Email: Patricia.Ward-Scaltsas@ed.ac.uk |
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